手法を比較

	NSGA-III	多目的最適化	Particle Swarm Optimization (PSO)
分野	オペレーションズ・リサーチ	シミュレーション	最適化
系統	Machine learning	Process / pipeline	Process / pipeline
提唱年	2014	1896 (concept); 1989–2002 (evolutionary algorithms era)	1995
提唱者	Kalyanmoy Deb and Himanshu Jain	Vilfredo Pareto (concept); modern computational formulation by Goldberg and Deb et al.	—
種類	algorithm	Optimization framework	Population-based metaheuristic / swarm intelligence
原典	Deb, K., & Jain, H. (2014). An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part I: Solving problems with box constraints. IEEE Transactions on Evolutionary Computation, 18(4), 577-601.	Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, Chichester. ISBN: 9780471873396	Kennedy, J. & Eberhart, R. (1995). Particle Swarm Optimization. IEEE International Conference on Neural Networks (ICNN), 1942-1948.
別名	NSGA-III algorithm, NSGA-III evolutionary, many-objective optimization	MOO, Multi-Criteria Optimization, Vector Optimization, Pareto Optimization	PSO, swarm intelligence optimization, Parçacık Sürü Optimizasyonu (PSO)
関連	2	3	6
概要	NSGA-III (Non-dominated Sorting Genetic Algorithm III), developed by Kalyanmoy Deb and Himanshu Jain in 2014, is a state-of-the-art evolutionary algorithm for many-objective optimization problems. It extends the popular NSGA-II algorithm with reference-point-based selection, enabling effective handling of problems with three or more conflicting objectives.	Multi-Objective Optimization (MOO) is a mathematical and computational framework for finding solutions that simultaneously optimize two or more conflicting objective functions. Rather than collapsing all goals into a single scalar, MOO produces a set of trade-off solutions — the Pareto front — from which a decision-maker selects according to preference. It is widely used in engineering design, operations research, logistics, economics, and policy analysis.	Particle Swarm Optimization (PSO) is a population-based metaheuristic algorithm introduced by Kennedy and Eberhart in 1995, inspired by the collective movement of bird flocks and fish schools. Each candidate solution — called a particle — moves through the search space by updating its velocity and position based on its own best experience and the best experience of the entire swarm, enabling fast convergence across continuous optimization problems.